How To Build A Decision Support System
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1 Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig
2 13. Decision Support Systems 13. Decision Support Systems (DSS) 13.1 Marketing Models 13.2 Supply Chain Management DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 2
3 13.0 DSS - Introduction Decision support systems (DSS) Are interactive, flexible, and adaptable content based information systems Developed for supporting the solution of a non- structured management problem for improved decision-making It utilizes data, it provides easy user interface, and it allows for the decision maker s own insights DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 3
4 13.0 DSS - Introduction DSS evolve as they develop The support for the decision layer is provided by traditional approaches, data mining and data warehousing with OLAP DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 4
5 13.0 DSS - Introduction Traditional approaches Common mathematical modeling e.g., what-if-analysis Non-rigorous modeling Data-driven Rule-based systems (RBS) Data Warehousing Online Analytical Processing (OLAP) Data-based decision support Modeling Conceptual modeling Logical modeling Physical modeling ETL-Processes DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 5
6 13.0 DSS - Introduction Data Mining Association rule mining Sequence patterns and time series Classification Clustering In DSS the key word is decision-making DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 6
7 13.0 DSS - Introduction Decision-making is a process of making the choice including Assessing the problem Collecting and verifying information Identifying alternatives Anticipating consequences of decisions Making the choice using sound and logical judgment based on available information Informing others of decision and rationale Evaluating decisions DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 7
8 13.0 Decisions Decision problem options (alternatives) goals FIND the option that bestsatisfies thegoals RANK options according to the goals ANALYSE, JUSTIFY, EXPLAIN,, the decision DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 8
9 13.0 Decisions Types of decisions Easy (routine, everyday) vs. difficult (complex) One-time vs. recurring One-stage vs. sequential Single objective vs. multiple objectives Individual vs. group Structured vs. unstructured Tactical, operational, strategic DSS address complex decisions DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 9
10 13.0 Complex Decisions Characteristics of complex decisions Novelty There was no prior similar decision Unclearness Incomplete knowledge about the problem Uncertainty Outside events that cannot be controlled Multiple objectives (possibly conflicting) Maximize economic benefits vs. minimize environmental costs Group decision-making Important consequences of the decision Limited resources DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 10
11 13.0 Decision-Making Decision-making (DM) Human DM Decision Sciences Machine DM Decision Systems Switching circuits Processors Computer programs Systems for routine DM Autonomous agents Space probes DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 11
12 13.0 Decision-Making Decision-making Decision Sciences Decision Systems Normative Decision Theory Utility Theory Game Theory Theory of Choice Descriptive Cognitive Psychology Social and Behavioral Sciences Decision Support Automated Control Fuzzy Logic Expert Systems DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 12
13 13.0 Decision Support Decision support Methods and tools for supporting people involved in the decision-making process Central Disciplines Operations research and management sciences Decision analysis Decision support systems DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 13
14 13.0 DSS - Introduction DSS capabilities Support for problem-solving phases Intelligence, design, choice, implementation, monitoring Support for different decision frequencies, e.g.: Ad hoc DSS: decisions that come up once in every 5 years (e.g., where should a company open a new distribution center?) Institutional DSS: decisions that repeat (e.g., what should the company invest in?) DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 14
15 13.0 DSS Capabilities Support for different problem structures Highly structured problems: known facts and relationships Semi-structured problems: facts unknown or ambiguous, relations vague E.g., which person to hire for a position? Support for various decision-making levels Operational level Daily decisions Tactical level Planning and control Strategic level Long-term decisions DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 15
16 13.0 DSS - Introduction DSS architecture Information resources The analytical engine The user interface DW Model management External models Knowledge-based subsystem Graphical User Interface DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 16
17 13.0 DSS Architecture The database management subsystem Captures/extracts data for inclusion in a DSS database Updates (adds, deletes, edits, changes) data records and files Interrelates data from different sources Retrieves data from the database for queries and reports DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 17
18 13.0 DBM Subsystem Provides comprehensive data security (protection from unauthorized access, recovery capabilities, etc.) Handles personal and unofficial data so that users can experiment with alternative solutions based on their own judgment Tracks data use within the DSS Manages data through a data dictionary DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 18
19 13.0 DSS Architecture The model management subsystem (MMS) Strategic models: non routine mergers, impact analysis, capital budgeting Tactical Models: allocation & Control labor requirements, sales promotion planning Operational Models: routine-day-to-day production scheduling, inventory control, quality control Analytical Models: SPSS, data mining DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 19
20 13.0 MMS Major functions of the MMS Creates models easily from scratch or from existing models Allows users to manipulate models so that they can conduct experiments and sensitive analysis e.g., whatif or goal seeking analysis Manages and maintains the model base e.g., Store, access, run, update, link, catalog and query DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 20
21 13.0 DSS Architecture The knowledge based subsystem Component of more advanced DSS Provides expertise in solving complex unstructured and semi-structured problems Expertise is provided by an expert system or other intelligent system Leads to intelligent DSS Example of knowledge extraction subsystem is data mining DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 21
22 13.0 DSS Architecture The user interface Interactive, dialogue oriented, menu driven Intuitive, graphical, symbolic Consistent syntax and semantics, layout and symbolism Intelligent, context aware Customized For the non-technical user, the user interface is the system DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 22
23 13.0 DSS - Introduction Applications of DSS Marketing Models Supply Chain Management DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 23
24 13.1 Marketing Models Marketing decision processes are characterized by a high level of complexity Simultaneous presence of multiple objectives Countless alternative actions resulting from the combination of the major choice options Massive sales transactions data are available making DSS a important tool for reaching marketing intelligence DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 24
25 13.1 Marketing Models Marketing intelligence comprises 2 prominent topics Relational marketing (RM) Sales force management (SFM) DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 25
26 13.1 Marketing Models Relational marketing as DSS application Designed to create, maintain, and enhance strong relationships with customers and other stakeholders Application of predictive models to support relational marketing strategies E.g.: An insurance company wishes to select the most promising market segment to target for a new type of policy A mobile phone provider wishes to identify those customers with the highest probability of churning DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 26
27 13.1 Relational Marketing Why is RM important? It costs five times as much to attract a new customer as it does to keep a current one satisfied It is claimed that a 5% improvement in customer retention can cause an increase in profitability of between 25-85% depending on the industry Likewise, it is easier to deliver additional products and services to an existing customer than to a first-time buyer DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 27
28 13.1 Relational Marketing RM strategies revolve around the following choices Products Services Distribution channels Segments Relational marketing Sales processes Prices Promotion channels DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 28
29 13.1 Relational Marketing How do we implement RM? Using pattern recognition and machine learning models on a company s DW it is possible to derive different segmentations of the customers which are then used to design and target marketing actions DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 29
30 13.1 Relational Marketing Cycle of RM analysis, phases: 1. Exploration of the data available for each customer 2. Identify market segments by using inductive learning models 3. Knowledge of customer profiles is then used to design marketing actions 4. The designed actions are Collect information on translated into promotional customers campaigns which generate in turn new information for subsequent analyses Perform optimized and targeted actions Plan actions based on knowledge Identify segments and needs DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 30
31 13.1 Customer Relations General statistics show The average business never hears from 96% of its unhappy customers 91% never come back Dissatisfied customers may tell 9-10 people about their experience Every positive experience is told to 4-5 people For every complaint received the average business in fact has 26 customers with a similar concern DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 31
32 13.1 Customer Relations Of the customers who register a complaint, as many as 70% will do business again with your organization, if the complaint is resolved effectively This figure goes up to 95% if the complaint has been resolved quickly 40% of complaints are the result from customer mistakes or incorrect expectations A complaint that is handled efficiently is actually better than no complaint at all Customers who complain and get satisfactory results are 8% more loyal than if no complaint at all DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 32
33 13.1 Customer Relations Important part of RM is customer relationship management (CRM) CRM The software tools which allow tracking and analysis of each customer's purchases, preferences, activities, tastes, likes, dislikes, and complaints Enterprise vendors/products Oracle/Siebel, SAP, Salesforce.com, Amdocs, Microsoft Dynamics Open source tools Opentaps, Tunesta, Compiere, XRMS, SugarCRM DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 33
34 13.1 Customer Relations E.g., XRMS Contact information screen DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 34
35 13.1 Customer Relations Aspects of CRM systems Operational Collaborative Analytical DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 35
36 13.1 CRM Operational CRM Provides support to "front office" business processes, including sales, marketing and service Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database when necessary Main benefits is that customers can interact with different people in a company over time without having to describe the history of their interaction each time DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 36
37 13.1 CRM Collaborative CRM Covers aspects of a company's dealings with customers that are handled by various departments within a company E.g., sales, technical support and marketing Staff members from different departments can share information collected when interacting with customers E.g., feedback received by customer support agents can provide other staff members with information on the services and features requested by customers Goal of collaborative CRM is to use information collected by all departments to improve the quality of services provided by the company DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 37
38 13.1 CRM Analytical CRM Analyzes customer data for a variety of purposes: Design and execution of targeted marketing campaigns to optimize marketing effectiveness Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention Analysis of customer behavior to aid product and service decision making e.g., pricing, new product development Management decisions, e.g. financial forecasting and customer profitability analysis Prediction of the probability of customer defection (churn) Acquisition? Cross-selling? Up-selling? Retention? Churn? Let s see the lifetime of a customer DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 38
39 13.1 Relational Marketing Lifetime of a customer Lost proposal Before becoming a customer, an individual may receive repeated proposals from the enterprise to win him/her as a customer Acquisition The individual becomes customer DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 39
40 13.1 Lifetime of a customer Cross/up-selling: getting more business from current customers by selling them additional or complementary services Retention: the continuous attempt to satisfy and keep current customers actively involved in conducting business Highly satisfied customers are Less price sensitive More likely to talk favorably about you More likely to refer you to others Remain loyal for longer DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 40
41 13.1 Lifetime of a customer Churn (defection): the percentage of customers who leave a business in one year Interruption: customers leaving a business. Possible reasons are that they: Die Move away Leave for competitive reasons Are dissatisfied Quit because of an attitude of indifference DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 41
42 13.1 Marketing Models Sales force management (SFM) Management of the whole set of people and roles that are involved with different tasks and responsibilities in the sales process Why SFM? It plays a critical role in: The profitability of an enterprise The implementation of the relational marketing strategy DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 42
43 13.1 Sales force management Designing the sales network and planning agents activities involve complex decision making tasks Remaining activities are operational and sales force automation (SFA) software can be used SFM decision-making process can be grouped in 3 components each interacting with each other Design Planning Assessment Design Sales force management Planning Assessment & control DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 43
44 13.1 Sales force management Design During start-up phase or during restructuring Includes 3 types of decisions Organizational structure Sizing Sales territories DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 44
45 13.1 Design Organizational structure May take different forms corresponding to hierarchical agglomerations of agents by group, products, brand or geographical area In order to determine the organizational structure it is necessary to analyze the complexity of customers products and sales activities Decide whether and to what extent the agents should be specialized DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 45
46 13.1 Design Sizing Decide the number of agents that should operate in the selected structure Depends on several factors Number of customers, prospects, sales area coverage estimated time for each call, the agents traveling time, etc. Conflicting goals Reduction in costs due to decreasing sales force size is often followed by a reduction in sales and revenues DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 46
47 13.1 Design Sales territories Deciding on assigning territories to agents Depends on factors such as The sales potential of the geographical areas The time required to travel from an area to another The availability time of each agent Purpose of assignment is to determine a balanced situation between sales opportunities in each territory to avoid disparities among agents DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 47
48 13.1 Sales force management Planning Decision-making process involving the assignment of sales resources structured and sized during design phase, to market entities E.g., sales resources Work time, budget E.g., market entities Products Market segments Distribution channels Customers DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 48
49 13.1 Sales force management Assessment Measure the effectiveness and efficiency of the individuals in order to decide incentives and remuneration schemes Define adequate evaluation criteria that take into account the personal contribution of each agent having removed effects due to area or product characteristics DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 49
50 13.1 Sales force management Sales Force Automation software Most CRM tools include SFA functionality Enterprise vendors/products Oracle/Siebel, SAP, Salesforce.com, Microsoft Dynamics, Netsuite Open source tools XRMS, SugarCRM, Vtiger DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 50
51 13.1 Sales force management DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 51
52 13.2 Supply Chain Management For producing industries, another field of business operation is of great importance: Supply chain management (SCM) A supply chain summarizes the logistic and production processes of a single enterprise as well as a network of companies Covers the flow of materials and products from the raw material down to the end product at the customer Contains acquisition of raw materials, production, transportation, storage, DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 52
53 13.2 Supply Chain Management Within a single company, internal supply chain can be modeled and optimized Contain aspects of martial purchase, production and distribution Internal Supply Chain Suppliers Purchasing Production Distribution Customers Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 53
54 13.2 Supply Chain Management However, global supply chains may form complex networks of various material flows and costs Recycling 1 European Suppliers European Plant European Assembly US Suppliers Main Plant Asian Assembly European Market US Assembly Asian Market Asian Suppliers Asian Plant Kit Supplier Recycling 2 US Market Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 54
55 13.2 Supply Chain Management Supply chain management is about managing and optimizing those complex supply networks Eliminating excess inventory Improvise on-time delivery performance Maximize the value of procurement Minimize transport costs Minimize storage costs Etc. Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 55
56 13.2 Supply Chain Management Steps of SCM Plan (strategic portion of SCM) Strategy for managing all the resources that go toward meeting customer demand Developing a set of metrics to monitor the performance of the supply chain so that it is efficient, costs less and delivers high quality Source Choose suppliers to deliver the goods and services Develop a set of pricing, delivery and payment processes with suppliers Create metrics for monitoring and improving the relationships Put together processes for managing goods and services inventory, including receiving and verifying shipments, transferring them to the manufacturing facilities and authorizing supplier payments Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 56
57 13.2 Supply Chain Management Make (manufacturing step) Schedule the activities necessary for production, testing, packaging and preparation for delivery Most metric-intensive portion of the supply - measure quality levels, production output and worker productivity Deliver (the logistics part) Coordinate the receipt of orders, develop a network of warehouses, pick carriers to get products to customers and set up an invoicing system to receive payments Return Receive and manage defective or excess products Recycle used products Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 57
58 13.2 Supply Chain Management For solving these tasks, SCM has to span across most other enterprise management areas Thus, software solutions are usually very diverse and customized Highly dependent on data from all branches of business Logistics Product Lifecycle Management Procurement Supply Chain Strategy Supply Chain Management Asset Management Supply Chain Planning Supply Chain Enterprise Applications Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 58
59 13.2 Supply Chain Management The traditional approach for optimizing supply chains was severely hampered by the unavailability of necessary data Thus, usually only future demand was forecast as good as possible, using statistical trending and best fit techniques Only high level data necessary e.g. by weekly data by product category and customer group For dealing with unpredictability, security margins are added Based on the estimates, the supply chain could be optimized Capacity Planning Bill of Material problems Network flow optimization etc. Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 59
60 13.2 Supply Chain Management However, due to improved data warehouse strategies, more dynamic and fine-grained optimizations are possible Forecasting at much finer-granularity e.g. calculate the best inventory level per article for each store So called model stock Allows for new optimization techniques Simulation Stochastic models Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 60
61 13.2 Supply Chain Management Include wider verity of metrics Stackability constraints Load and unloading rules Palletizing logic Warehouse efficiency Shipping air minimization Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 61
62 13.3 The Mondrian System Mondrian Open source OLAP engine provided by Pentaho Based on ROLAP technology Is able to work with any major DBMS Terradata, Oracle, IBM DB2, Sybase, Microsoft SQL Server, Microsoft Access, MySQL, Informix, PostgreSQL, etc. Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 62
63 The End DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 63
64 13 Thank You! I hope you enjoyed the lecture and learned at least some interesting stuff Next semester s master courses: Multimedia Databases, XML Databases, GIS Knowledge-Based Systems and Deductive Databases Wolf-Tilo Balke IfIS TU Braunschweig 64
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